In contrast to the metric learning approach of SyncNet, Chung et al. learned audio-visual embeddings as a multiclass classification task [1]}. Their method classifies one visual frame into one multiple possibly matching audio frames obtained at \(N\)  different locations. The decisions are integrated in order to obtain a global shift.
